Extended live-tracking and quantitative characterization of wound healing and cell migration with SiR-Hoechst

Abstract

Cell migration is essential to many life processes, including immune response, tissue repair, and cancer progression. A reliable quantitative characterization of the cell migration can therefore aid in the high throughput screening of drug efficacy in wound healing and cancer treatments. In this work, we report what we believe is the first use of SiR-Hoechst for extended live tracking and automated analysis of cell migration and wound healing. We showed through rigorous statistical comparisons that this far-red label does not affect migratory behavior. We observed excellent automated tracking of random cell migration, in which the motility parameters (speed, displacement, path length, directionality ratio, persistence time, and direction autocorrelation) obtained closely match those obtained from manual tracking. We also present an analysis framework to characterize the healing of a scratch wound from the perspective of single cells. The use of SiR-Hoechst is advantageous for the crowded environments in wound healing assays because as long as cell nuclei do not overlap, continuous tracking can be maintained even if there is cell-cell contact. In this paper, we report wound recovery based on the number of cells migrating into the wound over time, normalized by the initial cell count prior to the infliction of the wound. This normalized cell count approach is impervious to operator bias during the arbitration of wound edges and is also robust against variability that arises due to differences in the cell density of different samples. Additional wound healing characteristics were also defined based on the evolution of cell speed and directionality during healing. Not unexpected, the wound healing cells exhibited much higher tendency to maintain the same migratory direction in comparison to the randomly migrating cells. The use of SiR-Hoechst thus greatly simplified the automation of single cell and whole population analysis with high spatial and temporal resolution over extended periods of time.